A real-time air quality prediction method and device for a hydraulic tunnel

Abstract

The present invention relates to a method and device for real-time prediction of the air quality in a hydraulic tunnel, including the following: obtaining the concentration value of a target monitoring gas in a pre-selected hydraulic tunnel; inputting the concentration value of the target monitoring gas into an improved Gaussian diffusion model to obtain the concentration distribution of the target detection gas in the pre-selected hydraulic tunnel; based on the concentration distribution, finding the area where the concentration is lower than the preset concentration threshold of the target monitoring gas and marking the area; visually displaying the concentration distribution and the area marking content. The present invention can determine whether there is an area that needs to be warned in any area of the hydraulic tunnel and mark it, and finally visually display the overall situation. The present invention can conduct real-time monitoring of the air quality in the hydraulic tunnel, ensuring the stable progress of subsequent construction operations.

Description

Technical Field

[0001] The present invention relates to the technical field related to air data monitoring, and particularly to a method and device for real-time prediction of air quality in a hydraulic tunnel. Background Art

[0002] In tunnel construction, a ventilation system is necessary, which can provide sufficient fresh air in the tunnel, dilute, disperse and remove harmful gases, and reduce the dust concentration.

[0003] The existing control method of the tunnel ventilation system is to monitor the concentration of relevant substances through relevant sensors, and control the ventilation system to perform corresponding regulation according to the periodic monitoring situation.

[0004] The disadvantages of the above method are that after the initial monitoring ventilation of the tunnel is completed, there will be a long monitoring blank period, during which the change of air quality in the tunnel cannot be normally monitored. If toxic or combustible gases are generated during the construction process, subsequent normal treatment will not be possible, which may cause casualties, project duration extension and other situations. Summary of the Invention

[0005] The purpose of the present invention is to at least solve one of the deficiencies of the prior art, and provide a method and device for real-time prediction of air quality in a hydraulic tunnel.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] Specifically, a method for real-time prediction of air quality in a hydraulic tunnel is proposed, including the following:

[0008] Obtain the concentration value of the target monitoring gas in the pre-selected hydraulic tunnel;

[0009] Input the concentration value of the target monitoring gas into an improved Gaussian diffusion model to obtain the concentration distribution of the target detection gas in the pre-selected hydraulic tunnel;

[0010] Based on the concentration distribution, find out the area where the concentration is lower than the concentration threshold of the preset target monitoring gas and mark the area;

[0011] Visualize the concentration distribution and the area marking content.

[0012] Furthermore, specifically, the method for obtaining the concentration value of the target monitoring gas in the pre-selected hydraulic tunnel includes,

[0013] Set monitoring sensors or analyzers for different gases in the pre-selected hydraulic tunnel, and obtain the concentration value of the target monitoring gas by reading the values of the monitoring sensors or analyzers corresponding to the target monitoring gas.

[0014] Further, specifically, the improved Gaussian diffusion model includes

[0015] ,

[0016] where the time integral term represents the cumulative contribution of all released puff clouds of the target monitoring gas concentration from the historical moment to the current moment , and the integration range is 0 to t, i.e., the current time; is the source strength, representing the release rate of the target monitoring gas at time , , is the gas concentration change rate, is the tunnel volume, is the concentration value of the target monitoring gas, is the air change flow rate of the ventilation system in the hydraulic tunnel; u(τ) is the wind speed field, the wind speed varying with time, and the advective displacement of the puff cloud from to to needs to be calculated through integration; n is the mirror source superposition parameter, simulating the infinite reflections between the ground and the tunnel roof, and the value range of n is -N to N; H represents the emission height of the pollution source; represents the tunnel height; , , are the diffusion parameters. Assuming that the gas properties are the same as the average atmospheric gas properties, the diffusion parameters vary with changing, , where and are the experimentally calibrated parameters, and the value of i is x, y, z. If the gas properties are different from the average atmospheric gas properties, corresponding corrections are required; the additional correction term is the correction for gases with properties different from the average atmosphere.

[0017] Further, specifically, the types of the target monitoring gas include oxygen, carbon dioxide, carbon monoxide, sulfur dioxide, and hydrogen sulfide, and the concentration acquisition is respectively carried out through an oxygen sensor, a carbon dioxide analyzer, a carbon monoxide sensor, a hydrogen sulfide sensor, and a gas analyzer based on ultraviolet spectroscopy technology.

[0018] Further, the method further includes presetting a scoring mechanism, calculating scores for different areas in the hydraulic tunnel to judge the air quality situation in these areas, and processing according to the corresponding treatment measures for the preset air quality situation.

[0019] Further, specifically, visualizing the concentration distribution and the regional marking content includes

[0020] Input the concentration distribution and the area marking content into a pre-trained neural network, and the neural network outputs a visualization result.

[0021] The present invention also provides a device for real-time prediction of the air quality in a hydraulic tunnel, including the following:

[0022] A data acquisition module for acquiring the concentration values of target monitoring gases in a pre-selected hydraulic tunnel.

[0023] A concentration distribution calculation module for inputting the concentration values of the target monitoring gases into an improved Gaussian diffusion model to obtain the concentration distribution of the target detection gases in the pre-selected hydraulic tunnel.

[0024] An area marking module for finding and marking the areas where the concentration of the target monitoring gas is lower than the preset concentration threshold based on the concentration distribution.

[0025] A visualization display module for visually displaying the concentration distribution and the area marking content.

[0026] The beneficial effects of the present invention are as follows:

[0027] The present invention provides a method and a device for real-time prediction of the air quality in a hydraulic tunnel. By proposing an improved Gaussian diffusion model, the data collected by the arranged sensors is used to estimate the concentration distribution of the target monitoring gases in the pre-selected hydraulic tunnel. Then, based on the pre-established concentration threshold of the target monitoring gas, it is judged whether there are areas that need to be warned in any area of the hydraulic tunnel and marked. Finally, the overall situation is visually displayed. The present invention can monitor the air quality in the hydraulic tunnel in real time, ensuring the stable progress of subsequent construction operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] By describing the embodiments shown in the accompanying drawings in detail, the above and other features of the present disclosure will become more obvious. The same reference numerals in the drawings of the present disclosure represent the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0029] Figure 1 The flowchart of a method for real-time prediction of the air quality in a hydraulic tunnel according to the present invention is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The concept, specific structure, and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the purpose, solution, and effects of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0031] Embodiment 1. Referring to Figure 1 , the present invention provides a method for real-time prediction of the air quality in a hydraulic tunnel, including the following:

[0032] Step 110: Obtain the concentration value of the target monitoring gas in the pre-selected hydraulic tunnel.

[0033] Step 120: Input the concentration value of the target monitoring gas into the improved Gaussian diffusion model to obtain the concentration distribution of the target detection gas in the pre-selected hydraulic tunnel.

[0034] Step 130: Based on the concentration distribution, find the area where the concentration is lower than the preset concentration threshold of the target monitoring gas and mark this area.

[0035] Step 140: Visualize the concentration distribution and the area marking content.

[0036] In this Embodiment 1, by proposing an improved Gaussian diffusion model, the data collected by the arranged sensors is used to estimate the concentration distribution of the target monitoring gas in the pre-selected hydraulic tunnel. Then, according to the pre-established concentration threshold of the target monitoring gas, it is judged whether there is an area that needs to be warned in any area of the hydraulic tunnel and marked. Finally, the overall situation is visualized. The present invention can monitor the air quality in the hydraulic tunnel in real time, ensuring the stable progress of subsequent construction operations.

[0037] As a preferred embodiment of the present invention, specifically, the method for obtaining the concentration value of the target monitoring gas in the pre-selected hydraulic tunnel includes

[0038] Setting monitoring sensors or analyzers for different gases in the pre-selected hydraulic tunnel, and obtaining the concentration value of the target monitoring gas by reading the values of the monitoring sensors or analyzers corresponding to the target monitoring gas.

[0039] As a preferred embodiment of the present invention, specifically, the improved Gaussian diffusion model includes

[0040]

[0041] Among them, the time integral term represents the concentration of the target monitoring gas from the historical moment to the current moment The cumulative contribution of all the released smoke plumes (the integration range is from 0 to the current time t); is the source strength, representing the moment The release rate of the target monitoring gas; u(τ) is the wind speed field, the wind speed varying with time, which needs to be integrated to calculate the advective displacement of the smoke plume from to ; n is the mirror source superposition parameter, simulating the infinite reflections between the ground and the tunnel roof, with the value of n ranging from -N to N (N can take values from -2 to 2); H represents the emission height of the pollution source; represents the tunnel height; , , are the diffusion parameters. If the gas properties are the same as the average atmospheric gas properties, the diffusion parameters vary with the age of the smoke plume, , where and are the experimentally calibrated parameters. If the gas properties are different from the average atmospheric gas properties, corresponding corrections are needed; the additional correction term is the correction for gases with properties different from the average atmosphere.

[0042] In this preferred embodiment, an improved Gaussian diffusion model is constructed through the following ideas,

[0043] For a large amount of continuously collected gas data, a scientific statistical method is used to establish a mathematical model for air quality monitoring - the Gaussian diffusion model. By combining the emission inventory, meteorological data, and monitoring values, the concentration distribution and spatial diffusion trend of various gases in the region can be evaluated, and the pollution levels of different sources can be estimated, which is very important for accurately judging the current situation of regional air quality and source control. Through statistical analysis of historical monitoring data, the spatio-temporal distribution laws, change trends, and influencing factors of various air concentrations can be clarified, thus providing a basis for subsequent simulation prediction and pollution control.

[0044] The Gaussian diffusion model adopts the turbulent statistical theory system. In the turbulent diffusion theory (K theory) and statistical theory, it is considered that any particle in the y and z axes in atmospheric diffusion conforms to the normal distribution. A coordinate system with the pollution source as the center is established,

[0045]

[0046] This formula is the ideal state continuous point source Gaussian diffusion model, which has good results and applicability for the calculation of atmospheric diffusion under flat terrain conditions. Therefore, the Gaussian diffusion model is improved. By improving the Gaussian diffusion model, the air quality in the tunnel can be predicted while the instrument is monitoring in real time.

[0047] In the actual process of tunnel excavation, due to the existence of the top and bottom plates and surrounding rocks, the diffusion of the target monitoring gas is bounded. Therefore, it is assumed that the ground is a mirror surface, which has a total reflection effect on the target monitoring gas, and the image source method is used for processing.

[0048] First, determine the gas to be observed. Then, based on the neural network, analyze the spatial concentration distribution of the gas at that position, determine the concentration at any point p at that location, and divide the sum of the concentrations at that location into two parts: one part is the concentration of the target monitoring gas in the free space; the other part is the concentration of the target monitoring gas increased due to the reflection of the bottom plate. The concentration of the target monitoring gas at that location is equivalent to the sum of the concentrations of the target monitoring gas caused by the real source located at (0, 0, H) and the image source located at (0, 0, -H) at point p when there is no restriction. Therefore, the actual concentration X(x, y, z) at that location is obtained as X(x, y, z) = X1(x, y, z) + X2(x, y, z).

[0049] The Gaussian plume diffusion formula for elevated continuous point sources can be obtained.

[0050]

[0051] However, when analyzing the diffusion of the air target monitoring gas in the tunnel, the longitudinal diffusion is often ignored in the Gaussian plume diffusion formula. However, in long tunnels, the longitudinal diffusion has a significant impact on the distribution of the target monitoring gas (especially during low-speed ventilation). Therefore, the Gaussian plume formula is now improved by introducing the longitudinal diffusion into the Gaussian plume diffusion formula and also introducing the time parameter to predict the air target monitoring gas in the tunnel.

[0052]

[0053] But another problem arises. Are the diffusion parameters the same for different gases? The traditional Gaussian diffusion model studies the average atmospheric neutral molecules and there is no difference in the diffusion coefficient. However, there are many gases in the tunnel with properties different from atmospheric molecules, such as sulfur dioxide, whose air relative molecular mass is greater than 29 and it is an acidic gas. In this case, the diffusion parameters , , need to be corrected.

[0054] Since the longitudinal diffusion is dominated by flow turbulence and is independent of the gas species, the longitudinal diffusion parameter does not need to be corrected.

[0055] Carbon monoxide and oxygen have no sedimentation reaction, are chemically stable and close to atmospheric neutral molecules, so they do not need to be corrected. The diffusion parameters are: , where and are experimentally calibrated parameters (i = y, z).

[0056] Carbon dioxide is heavier than the average atmosphere, chemically inert, without sedimentation or chemical reactions, and the diffusion parameter is corrected as:

[0057]

[0058]

[0059] W is the width of the tunnel

[0060] Hydrogen sulfide is heavier than the average atmosphere and is soluble in water (significant wet sedimentation), and is partially oxidized to SO2. The diffusion parameter is corrected as:

[0061]

[0062] Sedimentation attenuation:

[0063] Additional term = exp(- , = 1~3 cm / s

[0064] Chemical attenuation:

[0065] Additional term = exp ,

[0066] Sulfur dioxide is heavier than the average atmosphere and is soluble in water (wet sedimentation), and can be oxidized to sulfate. The diffusion parameter is corrected as:

[0067]

[0068] Sedimentation attenuation:

[0069] Additional term = exp(- , = 0.5~2 cm / s

[0070] Chemical attenuation:

[0071] Additional term = exp ,

[0072] By correcting the diffusion parameters of various gases, the final improved Gaussian plume diffusion formula can be obtained:

[0073]

[0074] Among them, the additional correction term refers to sedimentation attenuation or chemical attenuation. If both sedimentation attenuation and chemical attenuation exist, the additional correction term takes the product of the two.

[0075] The basic principle of the concentration change method:

[0076] First, the tunnel is a closed or semi-closed finite space, and the release and ventilation of gas jointly affect the change of its concentration. According to the mass conservation equation: gas accumulation = release - ventilation carried out, the source strength formula can be obtained as follows:

[0077] Gas accumulation = release - ventilation carried out,

[0078] ,

[0079] After arrangement, we get ,

[0080] is the source strength, unit: ,

[0081] is the gas concentration change rate, unit: ,

[0082] is the tunnel volume, unit: ,

[0083] is the current gas concentration, unit: ,

[0084] is the air change flow rate of the ventilation system, unit: ,

[0085] First, the concentration can be obtained by the gas sensor installed in the tunnel, and can also be obtained by the wind speed sensor in the tunnel, then is determined by the geometric shape of the tunnel,

[0086] is the gas concentration change rate, and the concentration-time curve can be fitted through the gas sensor data to calculate the average concentration change rate during a certain period ,

[0087] This formula has good applicability in a tunnel with little air flow change and closed.

[0088] By obtaining the wind speed in this area through the wind speed sensor and substituting it into the improved Gaussian plume diffusion model, a prediction model of the gas concentration can be obtained. Finally, by substituting the corresponding data into the MATLAB program, the gas concentration distribution map of this gas in this area during a certain period can be obtained.

[0089] As a preferred embodiment of the present invention, specifically, the types of target monitored gases include oxygen, carbon dioxide, carbon monoxide, sulfur dioxide, and hydrogen sulfide, and their concentrations are collected by an oxygen sensor, a carbon dioxide analyzer, a carbon monoxide sensor, a hydrogen sulfide sensor, and a gas analyzer based on ultraviolet spectroscopy technology respectively.

[0090] In this preferred embodiment, corresponding sensors and analyzers are used to monitor the gas concentration in the tunnel, and finally, combined with a neural network and a control system, real-time monitoring of air quality can be achieved.

[0091] The main objects of gas environment monitoring in the tunnel are gases such as oxygen, carbon dioxide, carbon monoxide, sulfur dioxide, and hydrogen sulfide.

[0092] Therefore, it is adopted:

[0093] The oxygen sensor system adopts an S4-O type oxygen sensor. The measuring range of this sensor is 0~25%Vol, and the maximum measurement upper limit is 30%Vol. It has the characteristics of fast response speed and stable performance, and is widely used in the industrial measurement field.

[0094] For carbon dioxide, an infrared absorption method is used to design a carbon dioxide analyzer. Carbon dioxide has an absorption peak in the infrared region. At this wavelength, oxygen, nitrogen, carbon monoxide, and water vapor have no obvious absorption. Therefore, the infrared absorption method is an ideal method for measuring carbon dioxide in the air.

[0095] The carbon dioxide analyzer uses a wavelength as the measuring beam and a 3.9μm wavelength as the reference beam. The structure of the instrument adopts a single optical path and a time double-beam detection method to achieve the purpose of a double optical path. The measuring range of this instrument is 0-1.5% carbon dioxide, and the detection lower limit is 0.01%. There is a small electromagnetic pump in the instrument, which can automatically inhale ambient air for measurement. This method is the most convenient and commonly used method for measuring carbon dioxide in ambient air. When the instrument is placed in the environment, the carbon dioxide content can be directly measured.

[0096] The carbon monoxide sensor system adopts an S4-CO gas sensor to monitor the change of CO concentration. The measuring range of this sensor is 0~100%Vol, and it has the characteristics of fast response speed and high sensitivity.

[0097] The hydrogen sulfide sensor adopts an S4-H2S gas sensor to monitor the change of H2S concentration. The measuring range of this sensor is 0~0.5%Vol, and it has the characteristics of fast response speed and high sensitivity.

[0098] A gas analyzer using ultraviolet spectrum technology for the adoption of sulfur dioxide. This technology utilizes an ultraviolet light source and spectroscopic detection, and can directly detect the ultraviolet absorption characteristics of sulfur dioxide in the gas sample, achieving highly sensitive and rapid monitoring of the sulfur dioxide concentration. Its sampling frequency can reach the second level, meeting the requirements for continuous monitoring of industrial flue gas emissions.

[0099] By installing the above-mentioned sensors and analyzers in the tunnel and connecting them to a computer respectively, real-time monitoring of the air concentration in the tunnel can be achieved.

[0100] Then, the data monitored in the tunnel, as well as the air quality data and air quality maps of other underground operations in history, are sent to the neural network for simulation training to simulate the spatial distribution of various gases in each area of the tunnel. Finally, the neural network is used to statistically analyze the data.

[0101] As a preferred embodiment of the present invention, the method further includes presetting a scoring mechanism to calculate scores for different areas in the hydraulic tunnel to judge the air quality of the area, and processing according to the corresponding treatment measures preset for the air quality situation.

[0102] In this preferred embodiment, first, a judgment standard for the air quality in the tunnel is established. The judgment of air quality conforms to the barrel effect, that is, if a certain gas exceeds the allowable value, work in this area needs to be stopped for ventilation.

[0103]

[0104] Table 1

[0105] By referring to the various air allowable values and suitable values applicable to the interior during tunnel construction in the prior art and combining with the object to be studied this time, the above Table 1 is drawn.

[0106] Now, a simple function is established for the air quality in the area. When a certain gas is at the suitable value in Table 1, it gets 2 points; when it is within the allowable value but less than the suitable value, it gets 1 point; if it exceeds the allowable value, 10 points will be deducted (full score: 10 points).

[0107]

[0108] Table 2

[0109] According to the score situation, combined with Table 2 above, if the air quality is "excellent" or "good", the area can work normally without the need for emergency evacuation and ventilation. Among them, for the situation where the air quality is "good", the gas situation needs to be concerned;

[0110] If the air quality is "safe", the area needs to be ventilated in a timely manner. Construction is temporarily stopped or carried out briefly until ventilation is carried out;

[0111] If the air quality is "dangerous" and there is no construction, ventilation can be carried out; if construction is in progress, construction should be stopped immediately, the workers should be evacuated in an orderly manner, and rescue personnel should be arranged to conduct safety checks on the tunnel workers. At the same time, ventilate and change the air in this area of the tunnel.

[0112] As a preferred embodiment of the present invention, specifically, the concentration distribution and the regional marking content are visually displayed, including,

[0113] The concentration distribution and the regional marking content are input into a pre-trained neural network, and the neural network outputs the visual display result.

[0114] The present invention also proposes a device for real-time prediction of the air quality in a hydraulic tunnel, including the following:

[0115] A data acquisition module for acquiring the concentration value of a target monitoring gas in a pre-selected hydraulic tunnel;

[0116] A concentration distribution calculation module for inputting the concentration value of the target monitoring gas into an improved Gaussian diffusion model to obtain the concentration distribution of the target detection gas in the pre-selected hydraulic tunnel;

[0117] A regional marking module for finding and marking the area where the concentration of the target monitoring gas is lower than the preset concentration threshold based on the concentration distribution;

[0118] A visual display module for visually displaying the concentration distribution and the regional marking content.

[0119] In addition, when the present invention is applied, when a certain engineering team wants to carry out tunnel construction, first ventilate the tunnel that has been excavated to a certain depth or has a natural opening, and conduct air quality monitoring. After confirming safety, the construction team starts working. After working for a period of time, suddenly some workers feel dizzy and have difficulty breathing. Their companions hurriedly call for people to come to rescue, but it is found that many workers have various symptoms. Finally, the construction team is rescued urgently because most of the workers have mild poisoning, and the tunnel needs to be ventilated or left for a long time, which lengthens the construction period and causes economic losses.

[0120] In summary, if there is a method for real-time monitoring of air quality and prediction of toxic and harmful gases, the construction team will be able to detect toxic and harmful gases in a timely manner, and can predict whether there are toxic and harmful gases in the rock formation, which will ensure the safety of construction workers, and can timely ventilate the tunnel or change the construction plan to reduce the generation of toxic and harmful gases, minimizing the losses.

[0121] In addition, in each embodiment of the present invention, each functional module can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module.

[0122] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or system, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc., that can carry the computer program code.

[0123] Although the description of the present invention has been quite detailed and particularly describes several of the embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but rather should be regarded as providing a broad interpretation of these claims in light of the prior art by reference to the appended claims, thereby effectively covering the intended scope of the present invention. In addition, the present invention is described above with embodiments foreseeable by the inventors for the purpose of providing a useful description, and non-substantive modifications to the present invention that are not currently foreseeable can still represent equivalent modifications of the present invention.

[0124] As mentioned above, it is only a preferred embodiment of the present invention. The present invention is not limited to the above-mentioned implementation manners. As long as it achieves the technical effects of the present invention by the same means, it should fall within the protection scope of the present invention. Within the protection scope of the present invention, there can be various different modifications and variations to its technical solutions and / or implementation manners.

Claims

1. A real-time prediction method for the air quality of a hydraulic tunnel, characterized in that, It includes the following steps: Obtain the concentration value of the target monitoring gas in the pre-selected hydraulic tunnel; Input the concentration value of the target monitoring gas into the improved Gaussian diffusion model to obtain the concentration distribution of the target monitoring gas in the pre-selected hydraulic tunnel; Based on the concentration distribution, find the area where the concentration is lower than the preset concentration threshold of the target monitoring gas and mark the area; Visually display the concentration distribution and the area marking content; Specifically, the improved Gaussian diffusion model includes , where the time integral term represents the cumulative contribution of all released smoke plumes to the target monitored gas concentration from the historical moment to the current moment . The integration range is is the source strength, representing the release rate of the target monitored gas at time . , is the gas concentration change rate, is the tunnel volume, is the concentration value of the target monitored gas, is the air change flow rate of the ventilation system in the hydraulic tunnel; u(τ) is the wind speed field, the wind speed varying with time, and the advective displacement of the smoke plume from to to needs to be calculated through integration; n is the mirror source superposition parameter, simulating infinite reflections between the ground and the tunnel roof, and the value range of n is -N to N; H represents the emission height of the pollution source; represents the tunnel height; , , are the diffusion parameters. Assuming that the gas properties are the same as the average atmospheric gas properties, the diffusion parameters vary with , , where and are the experimentally calibrated parameters, and the value of i is x, y, z; the additional correction term is the correction for gases with properties different from the average atmospheric properties.

2. The real-time air quality forecasting method for a hydraulic tunnel according to claim 1, characterized in that, The method for obtaining the concentration value of the target monitoring gas in the pre-selected hydraulic tunnel includes Set monitoring sensors or analyzers for different gases in the pre-selected hydraulic tunnel, and obtain the concentration value of the target monitoring gas by reading the values of the corresponding monitoring sensors or analyzers of the target monitoring gas.

3. A real-time air quality forecasting method for hydraulic tunnels according to claim 2, characterized in that, The types of target monitoring gases include oxygen, carbon dioxide, carbon monoxide, sulfur dioxide, and hydrogen sulfide. The concentration is collected through an oxygen sensor, a carbon dioxide analyzer, a carbon monoxide sensor, a hydrogen sulfide sensor, and a gas analyzer based on ultraviolet spectroscopy technology respectively.

4. A real-time air quality prediction method for a hydraulic tunnel according to claim 3, characterized in that The method further includes presetting a scoring mechanism to calculate the scores of different areas in the hydraulic tunnel to judge the air quality of the area, and processing according to the corresponding treatment measures for the preset air quality.

5. A real-time air quality forecasting method for a hydraulic tunnel according to claim 1, characterized in that, Specifically, visually displaying the concentration distribution and the area marking content includes Input the concentration distribution and the area marking content into a pre-trained neural network, and the neural network outputs the visual display result.

6. An apparatus for real-time prediction of the air quality in a hydraulic tunnel, characterized in that, It includes the following: A data acquisition module for obtaining the concentration value of the target monitoring gas in the pre-selected hydraulic tunnel; A concentration distribution calculation module for inputting the concentration value of the target monitoring gas into the improved Gaussian diffusion model to obtain the concentration distribution of the target monitoring gas in the pre-selected hydraulic tunnel; An area marking module for finding the area where the concentration is lower than the preset concentration threshold of the target monitoring gas and marking the area based on the concentration distribution; A visual display module for visually displaying the concentration distribution and the area marking content; Specifically, the improved Gaussian diffusion model includes , where the time integral term represents the cumulative contribution of all released puff clouds of the target monitored gas concentration from the historical moment to the current moment . The integration range is is the source strength, representing the release rate of the target monitored gas at time . , is the gas concentration change rate, is the tunnel volume, is the concentration value of the target monitored gas, is the air change flow rate of the ventilation system in the hydraulic tunnel; u(τ) is the wind speed field, the wind speed varying with time, and the advective displacement of the puff cloud from to to needs to be calculated by integration; n is the mirror source superposition parameter, simulating infinite reflections between the ground and the tunnel roof, and the value range of n is -N to N; H represents the emission height of the pollution source; represents the tunnel height; , , are the diffusion parameters. Assuming that the gas properties are the same as the average atmospheric gas properties, the diffusion parameters vary with . , where and are the experimentally calibrated parameters, and the value of i is x, y, z; the additional correction term is the correction for gases different from the average atmospheric properties.

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